BRD4-Associated Chromatin Remodeling Signature Links Epigenetic Regulation to Immune Landscape in Ovarian Cancer

BRD4-Associated Chromatin Remodeling Signature Links Epigenetic Regulation to Immune Landscape in Ovarian Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article BRD4-Associated Chromatin Remodeling Signature Links Epigenetic Regulation to Immune Landscape in Ovarian Cancer Zidan Lin, Hongxi Chen, Jing Chen, Shuting Huang, Jiemei Hu, Chenfei Zhou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7030302/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2026 Read the published version in Journal of Ovarian Research → Version 1 posted 11 You are reading this latest preprint version Abstract Background Chromatin remodeling-related genes (CRRGs) are essential regulators of gene expression and tumor behavior. Their role in shaping the immune microenvironment and influencing prognosis in ovarian cancer (OV) remains largely unexplored. This study aimed to develop a CRRG-based prognostic signature and investigate its association with immune infiltration, particularly plasmacytoid dendritic cells (pDCs). Methods We integrated transcriptomic and clinical data from TCGA (n = 228) and ICGC (n = 111) ovarian cancer cohorts. Differentially expressed CRRGs associated with overall survival were identified and used to construct a prognostic signature via LASSO and Cox regression. Immune infiltration was analyzed using ssGSEA and validated by multiplex immunofluorescence (mIF). Correlations between BRD4 expression, pDC infiltration, and chemokine profiles were assessed using public databases and clinical specimens. Results An 11-gene CRRG-based immune-associated signature was established, effectively stratifying patients into high- and low-risk groups with significantly different overall survival in both the TCGA and ICGC cohorts. The risk score was an independent prognostic factor. Immune analyses revealed that high-risk patients exhibited reduced pDC infiltration and lower activation of antigen presentation-related pathways. BRD4 was identified as a key gene negatively correlated with pDC levels across datasets. High BRD4 expression was associated with poor survival and decreased expression of multiple pDC-attracting chemokines. mIF staining confirmed the inverse correlation between BRD4 expression and BDCA2 + pDC infiltration in OV tumor tissues. Conclusion This study proposes a novel CRRG-based prognostic signature linked to immune features in OV and highlights BRD4 as a potential regulator of pDC infiltration through suppression of chemokine expression. These findings provide insights into the interplay between epigenetic regulation and the immune landscape in OV, although the implications for immunotherapy responsiveness warrant further investigation. Ovarian cancer Chromatin remodeling Prognostic model Nomogram BRD4 Plasmacytoid dendritic cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Ovarian cancer (OV) remains one of the most lethal gynecologic malignancies worldwide, largely due to its insidious onset, high rate of metastasis, and frequent recurrence after initial therapy [1]. Despite advances in surgery and chemotherapy, the overall prognosis for patients with advanced-stage OV remains poor, with a 5-year survival rate below 50% [2]. Therefore, identifying reliable prognostic biomarkers and uncovering the biological mechanisms underlying immune evasion and disease progression are crucial for improving patient stratification and therapeutic outcomes. Chromatin remodeling-related genes (CRRGs) play critical roles in regulating gene expression, maintaining genomic stability [3], and orchestrating cellular responses to environmental signals, including immune stimuli [4, 5]. Emerging evidence suggests that dysregulation of chromatin remodeling is closely linked to cancer development [6] [7], progression [8], and the shaping of the tumor immune microenvironment (TIME) [9-12]. However, the prognostic value of CRRGs in ovarian cancer and their potential involvement in modulating anti-tumor immunity remain largely unexplored. Dendritic cells (DCs), particularly plasmacytoid dendritic cells (pDCs), are central players in anti-tumor immunity due to their ability to present antigens and initiate T cell responses [13-15]. Decreased infiltration or functional impairment of pDCs in tumors has been associated with immune escape and poor clinical outcomes [16, 17]. Nevertheless, the regulatory factors and molecular pathways affecting pDC infiltration in OV are not well defined. In this study, we aimed to construct an immune-associated prognostic signature based on differentially expressed CRRGs and to explore its relevance to the immune landscape of ovarian cancer. By integrating transcriptomic data from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), we developed and validated an 11-gene CRRG-based prognostic model. We further identified BRD4 as a key gene negatively associated with pDC infiltration and uncovered a potential BRD4–chemokine–pDC regulatory axis that may contribute to immune suppression and adverse prognosis in OV. These findings offer novel insights into the epigenetic regulation of the tumor immune microenvironment and provide potential targets for prognostic assessment and immunotherapy in ovarian cancer. Materials and Methods Ethical Statement This study was approved by the Institutional Review Board of Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou (KY2023-630-01). All procedures were performed in accordance with relevant institutional guidelines and regulations. Informed consent was obtained from all patients involved. Data Sources RNA sequencing (RNA-seq) data and corresponding clinical information for 376 ovarian cancer (OV) samples were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). Immune scores for the TCGA OV samples were retrieved from the ESTIMATE database (https://bioinformatics.mdanderson.org/estimate/disease/). After excluding samples lacking immune score data, a total of 228 TCGA OV samples were included as the training cohort. An external validation cohort comprising RNA-seq data and clinical information from 111 OV samples was downloaded from the International Cancer Genome Consortium (ICGC) (https://dcc.icgc.org/projects/OV-AU). Both TCGA and ICGC datasets are publicly available and were used in accordance with their respective data access policies and publication guidelines. Additionally, 126 patients with histologically confirmed ovarian cancer treated at Guangdong Provincial People’s Hospital were enrolled for multiplex immunofluorescence (mIF) validation, with written informed consent obtained from all participants. A total of 870 chromatin remodeling-related genes (CRRGs) were sourced from a previously published authoritative study (Supplementary Table S1) [4]. Construction and Validation of an Immune-Associated Prognostic Signature Based on CRRGs Gene expression data were normalized using the “limma” R package. TCGA OV patients were divided into high (n = 115) and low (n = 113) immune score groups based on the median immune score. Differentially expressed genes (DEGs) between these groups were identified using the “DESeq2” R package, applying the criteria of |log2 fold change| > 0 and false discovery rate (FDR) < 0.05. CRRGs with prognostic significance were identified via univariate Cox regression analysis. To avoid overfitting, a least absolute shrinkage and selection operator (LASSO) Cox regression model was applied using the “glmnet” R package. Tenfold cross-validation determined the optimal penalty parameter (λ) corresponding to the minimum partial likelihood deviance. The risk score for each patient was calculated using the following formula: where n is the number of selected genes, coef(i) is the regression coefficient, and Exp(i) is the normalized expression level of gene i . Patients were classified into high- and low-risk groups based on the median risk score. Kaplan–Meier (K-M) survival curves and log-rank tests were used to compare overall survival (OS) between risk groups. Optimal cutoff values for each gene were determined using the “surv_cutpoint” function from the “survminer” R package. The prognostic performance of the gene signature was assessed using time-dependent receiver operating characteristic (ROC) curves generated with the “survivalROC” R package [18]. The model was further validated in the ICGC cohort using identical statistical methods. Nomogram Construction and Evaluation Univariate and multivariate Cox regression analyses were conducted to determine whether the CRRG-based risk score and clinical variables served as independent prognostic factors. A nomogram integrating these factors was constructed to estimate 1-, 3-, and 5-year OS probabilities using the “rms” R package [19]. Calibration curves were generated to assess the predictive accuracy of the nomogram [20]. Immune Landscape and Pathway Analyses Single-sample gene set enrichment analysis (ssGSEA) was performed using the “GSVA” R package to quantify the infiltration scores of 16 immune cell types and the activity of 13 immune-related pathways. The annotated gene set used is detailed in Supplementary Table S2. Immune cell infiltration was further analyzed using the TIMER database (https://cistrome.shinyapps.io/timer/) [21], which provides estimates for B cells, CD4⁺ and CD8⁺ T cells, macrophages, neutrophils, and dendritic cells. Associations between gene expression, tumor-infiltrating lymphocytes (TILs), and chemokines were examined via the TISIDB platform (http://cis.hku.hk/TISIDB/index.php) [22]. The relative abundance of TILs was inferred using gene set variation analysis (GSVA) based on the normalized gene expression profiles. Survival Analysis The Kaplan–Meier Plotter tool (https://kmplot.com/analysis/) [23, 24] was used to explore associations between gene expression and OS in OV patients. Hazard ratios and log-rank P values were calculated to assess statistical significance. Multiplex Immunofluorescence (mIF) Staining All experiments involving human tissues were conducted under ethical approval and followed standard protocols. Formalin-fixed paraffin-embedded (FFPE) tumor samples from 126 OV patients were processed using the Opal 7-Color IHC Kit (Akoya Biosciences, Cat. No. NEL811001KT). After deparaffinization and antigen retrieval in sodium citrate buffer, sections were incubated with antibodies against BRD4 (Abcam, ab128874) and BDCA2 (Abcam, ab239077), followed by DAPI nuclear staining. Imaging was performed using the Vectra Polaris Quantitative Pathology Imaging System, and quantitative analysis was conducted with Phenochart software (version 1.0.12; Akoya Biosciences). Statistical Analysis All statistical analyses were conducted using R software (v4.2.2), SPSS (v25.0), and GraphPad Prism (v9.5). Differences between two groups were assessed using either the Student’s t-test (parametric) or the Mann–Whitney U test (non-parametric). Categorical variables were analyzed using Fisher’s exact test or the Chi-square test, as appropriate. Univariate and multivariate Cox regression analyses were applied to identify independent prognostic factors for OS. Correlations between variables were evaluated using Pearson’s or Spearman’s correlation coefficients. A two-tailed P-value < 0.05 was considered statistically significant. Results Construction and Validation of an Immune-Associated Prognostic Signature Based on CRRGs The detailed workflow of this study is illustrated in Figure 1. A total of 228 ovarian cancer (OV) patients from the TCGA-OV cohort and 111 OV patients from the ICGC (OV-AU) cohort were included in the final analysis. The clinical characteristics of these cohorts are summarized in Supplementary Table S3. Among the chromatin remodeling-related genes (CRRGs), 399 out of 870 genes (45.9%) were found to be differentially expressed between the high and low immune score groups. Of these, 26 genes were significantly associated with overall survival (OS) in univariate Cox regression analysis (Figure 2A). To refine the prognostic model, least absolute shrinkage and selection operator (LASSO) Cox regression was applied to the expression profiles of these 26 genes. Subsequently, multivariate Cox regression analysis was performed, and the results are presented in a forest plot (Figure 2B). An 11-gene immune-associated prognostic signature was ultimately established based on the optimal λ value derived from LASSO analysis (Supplementary Figure 1A, B). The risk score was calculated using the following formula: Risk score = (0.0031 × BRD4 ) + (0.0098 × CHD4 ) + (−0.0207 × ELP3 ) + (0.0012 × FBL ) + (−0.0029 × FOXA1 ) + (0.0052 × ING4 ) + (0.0029 × KMT2E ) + (−0.0050 × TADA1 ) + (0.0045 × TAF6 ) + (−0.0263 × TRIM27 ) + (−0.0100 × WDR77 ) Based on the median risk score, patients from the TCGA cohort were stratified into high-risk (n = 113) and low-risk (n = 115) groups (Supplementary Figure S2A). Kaplan–Meier survival analysis demonstrated that patients in the high-risk group had significantly poorer overall survival compared to those in the low-risk group (P < 0.0001, Figure 3A). Time-dependent receiver operating characteristic (ROC) curve analysis revealed that the risk score exhibited favorable predictive performance, with area under the curve (AUC) values of 0.748 at 3 years and 0.793 at 5 years (Figure 3B). To assess the robustness and generalizability of the prognostic signature, the same formula derived from the TCGA cohort was applied to the ICGC (OV-AU) cohort. Patients were classified into high-risk (n = 55) and low-risk (n = 56) groups based on the median risk score (Supplementary Figure S2B). Consistent with the TCGA findings, patients in the high-risk group exhibited significantly shorter survival durations compared to those in the low-risk group ( P = 0.0049, Figure 3C). The AUC values for the 11-gene signature in the ICGC cohort were 0.717 at 3 years and 0.659 at 5 years (Figure 3D), further supporting its prognostic utility. Independent Prognostic Analysis of the Signature and Construction of the Nomogram Univariate and multivariate Cox regression analyses were conducted to determine whether common clinicopathological variables and the risk score served as independent prognostic predictors for overall survival (OS). In the univariate analysis, age (HR = 1.4379, 95% CI = 1.0083–2.0505, P < 0.05) and clinical stage (HR = 2.7193, 95% CI = 1.0021–7.3790, P < 0.05) were significantly associated with OS in the TCGA cohort (Fig. 4A). Additionally, the risk score showed a strong correlation with OS in both the TCGA (HR = 3.2865, 95% CI = 2.2537–4.7925, P < 0.001) and ICGC (HR = 1.8289, 95% CI = 1.1908–2.8091, P = 0.0058) cohorts (Fig. 4A, C). Upon adjusting for other confounding factors, age remained an independent prognostic factor in the TCGA cohort (HR = 1.5412, 95% CI = 1.0775–2.2042, P < 0.05), and the risk score retained its independent prognostic value in multivariate analyses for both cohorts (TCGA: HR = 3.2808, 95% CI = 2.2379–4.8098, P < 0.001; ICGC: HR = 1.8243, 95% CI = 1.1877–2.8021, P = 0.006; Fig. 4B, D). These results indicate that both clinicopathological parameters and the CRRG-based prognostic signature independently contribute to survival prediction in ovarian cancer (OV). To provide individualized survival predictions, we developed a nomogram incorporating clinicopathological variables and the risk score to estimate 1-, 3-, and 5-year OS based on the TCGA cohort (Fig. 4E). The calibration curve demonstrated good agreement between predicted and observed survival outcomes (Fig. 4F). Immune Status Analysis in the TCGA and ICGC Cohorts To explore the relationship between immune status and the risk score, we performed single-sample gene set enrichment analysis (ssGSEA) to quantify immune cell infiltration and functional activity. Notably, the enrichment scores for antigen-presenting dendritic cells (aDCs), immature dendritic cells (iDCs), and plasmacytoid dendritic cells (pDCs) significantly differed between the high- and low-risk groups in the ICGC cohort ( P < 0.05; Fig. 5B). Specifically, pDC scores were significantly reduced in the high-risk group in both the TCGA and ICGC cohorts ( P < 0.05; Fig. 5A, B). Additionally, antigen presentation-related pathways—APC costimulation, HLA expression, and MHC class I signaling—were downregulated in the high-risk group of the TCGA cohort ( P < 0.05; Fig. 5C). These findings were supported by similar trends in the ICGC cohort, where T-cell coinhibition scores were reduced and type II IFN response scores were elevated in the high-risk group ( P < 0.05; Fig. 5D). Identification of a Key Gene Associated with pDC Infiltration To identify key CRRGs related to dendritic cell infiltration, we employed the TIMER database to assess correlations between the 11-gene signature and dendritic cell levels. BRD4, FBL, CHD4, MLL5 (KMT2E), TADA1, and TAF6 were negatively correlated with dendritic cell infiltration ( P < 0.05; Fig. 6A), with BRD4 showing the strongest inverse correlation. Further analysis using the TISIDB database confirmed a significant negative correlation between BRD4 expression and pDC infiltration ( P < 0.05; Fig. 6B). Based on RNA-seq data from the TCGA and ICGC cohorts, patients were stratified into BRD4-high and BRD4-low groups using the median expression level. Consistent with previous findings, ssGSEA analysis showed that pDC infiltration was significantly lower in BRD4-high patients in both cohorts ( P < 0.05; Fig. 6C). Kaplan–Meier survival analysis revealed that high BRD4 expression was associated with poorer OS (HR = 1.38, 95% CI = 1.04–1.85, P = 0.028), whereas high expression of CLEC4C (encoding BDCA2, a marker of pDCs) was positively associated with OS (HR = 0.71, 95% CI = 0.55–0.93, P = 0.011; Fig. 6D). Validation of the Correlation Between BRD4 Expression and pDC Infiltration To experimentally validate the association between BRD4 expression and pDC infiltration, we analyzed tumor samples from 126 OV patients using multiplex immunofluorescence (mIF) staining targeting BRD4 and BDCA2. Patients were stratified into BRD4-high and BRD4-low groups based on fluorescence intensity. The BRD4-high group showed more advanced FIGO stages (III–IV), higher lymph node and distant metastases, and elevated CA125 levels, though no differences were found in age, histological type, or grade (Supplementary Table S4). mIF confirmed that BDCA2 + pDC infiltration was significantly lower in the BRD4-high group (Fig. 7A, B). A significant negative correlation was observed between BRD4 expression and BDCA2 + pDC infiltration ( P < 0.0001, r = –0.514, Pearson's correlation; Fig. 7C), consistent with in silico findings. Moreover, patients with the BRD4 low BDCA2 high phenotype exhibited significantly prolonged OS compared to those with the BRD4 high BDCA2 low ( P = 0.0049; Fig. 7D). Identification of the BRD4–Chemokine–pDC Axis The infiltration of pDCs into the tumor microenvironment is often regulated by chemokines [25]. To elucidate the mechanistic role of BRD4 in modulating pDC recruitment, we explored its association with chemokine expression using the TISIDB database (Supplementary Fig. S3A). RNA-seq data from both the TCGA and ICGC cohorts were also analyzed. Consistent inverse correlations between BRD4 expression and five chemokines—CCL11, CCL17, CCL19, CXCL13, and XCL2—were observed across datasets (Fig. 8A, B; Supplementary Fig. S3B, C). Furthermore, TISIDB analysis revealed that infiltration levels of pDCs were positively correlated with the expression of these five chemokines ( P < 0.0001 for all; Fig. 8C). These findings suggest that BRD4 may suppress pDC infiltration by downregulating key pDC-attracting chemokines, providing a mechanistic explanation for the observed inverse relationship between BRD4 expression and pDC presence in the OV tumor microenvironment. Discussion In this study, we developed and validated an immune-associated prognostic signature based on chromatin remodeling-related genes (CRRGs) in ovarian cancer. By integrating transcriptomic and clinical data from two independent cohorts (TCGA and ICGC), we identified an 11-gene signature that effectively stratified patients into high- and low-risk groups with significantly different overall survival outcomes. The signature remained an independent prognostic factor after adjustment for conventional clinical variables, and its predictive performance was confirmed in both cohorts. Importantly, we uncovered a novel link between BRD4 expression, reduced plasmacytoid dendritic cell (pDC) infiltration, and poor prognosis, suggesting a potential epigenetic–immune axis in the ovarian tumor microenvironment. Chromatin remodeling has been increasingly recognized as a central regulatory mechanism in cancer, not only by altering transcriptional landscapes but also by influencing immune surveillance [26-28]. Among the 11 genes constituting our prognostic model, several (e.g., CHD4, KMT2E, BRD4) are well-established chromatin regulators implicated in oncogenic transcription, DNA repair, and cell cycle control [29-34]. Interestingly, some genes (e.g., ELP3, FOXA1, TRIM27) demonstrated protective roles [35-40], underscoring the complex and context-dependent functions of CRRGs in tumor biology. The application of LASSO and multivariate Cox regression ensured that the model captured the most informative and non-redundant prognostic features. A key finding of our study is the identification of BRD4 as a potential suppressor of pDC infiltration in the ovarian cancer microenvironment. BRD4, a member of the bromodomain and extra-terminal (BET) family, is known to regulate transcription of oncogenes and inflammatory mediators via binding to acetylated histones [41, 42]. We observed that high BRD4 expression correlated negatively with pDC infiltration, as determined by both in silico (ssGSEA, TIMER, TISIDB) and experimental validation using multiplex immunofluorescence in clinical samples. Notably, patients with high BRD4 and low BDCA2 + pDC levels exhibited the worst overall survival, highlighting the clinical relevance of this axis. Mechanistically, we propose that BRD4 may impair pDC recruitment by downregulating a specific subset of chemokines, including CCL11, CCL17, CCL19, CXCL13, and XCL2. These chemokines are known to mediate the recruitment and positioning of dendritic cells and other immune effectors within the tumor. The inverse correlation between BRD4 and these chemokines was consistently observed across databases and cohorts. Although the precise regulatory circuitry remains to be fully elucidated, it is plausible that BRD4, through transcriptional repression or chromatin compaction, directly or indirectly limits the expression of these chemokines, thereby contributing to an immunosuppressive microenvironment. Our findings add to the growing body of literature highlighting the interplay between epigenetic regulators and tumor immunity. Previous studies have shown that BET inhibitors can restore anti-tumor immunity by reprogramming tumor-associated macrophages, enhancing T cell infiltration, or modulating interferon responses [43] [44]. Our study extends this concept by implicating BRD4 in the regulation of pDC infiltration—a relatively underexplored cell type in ovarian cancer—thus providing a rationale for further exploration of BRD4 as an immunomodulatory target. Clinically, the CRRG-based signature and the BRD4–chemokine–pDC axis offer promising avenues for patient stratification and therapeutic intervention. The gene signature could potentially serve as a prognostic tool to identify high-risk patients who may benefit from closer surveillance or novel therapeutic strategies. Meanwhile, targeting BRD4 may not only suppress tumor proliferation but also reshape the immune microenvironment by restoring pDC recruitment and function. Combining BET inhibitors with immune checkpoint blockade or dendritic cell-based vaccines could be a promising strategy worth preclinical and clinical investigation. However, several limitations of this study should be acknowledged. First, although our model was validated in an external cohort and supported by mIF data, its predictive utility should be further tested in prospective, multi-center clinical studies. Second, the regulatory relationship between BRD4 and chemokine expression was inferred through correlation analysis and remains to be mechanistically verified using functional experiments such as chromatin immunoprecipitation (ChIP) or BRD4 knockdown assays. Third, while we focused on pDCs due to their immunoregulatory roles and clinical relevance, the broader impact of CRRGs on other immune cell types and stromal interactions warrants further exploration. Conclusion Our study presents a novel CRRG-based prognostic signature for ovarian cancer and identifies BRD4 as a key epigenetic regulator linked to impaired pDC infiltration and unfavorable prognosis. These findings underscore the importance of chromatin remodeling in shaping the immune landscape of ovarian tumors and highlight the potential of targeting epigenetic-immune pathways for therapeutic benefit. Abbreviations OV Ovarian Cancer CRRGs Chromatin Remodeling-Related Genes TIME Tumor Immune Microenvironment pDCs Plasmacytoid Dendritic Cells DCs Dendritic Cells TCGA The Cancer Genome Atlas ICGC International Cancer Genome Consortium OS Overall Survival MIF Multiplex Immunofluorescence RNA-seq RNA Sequencing DEGs Differentially Expressed Genes FDR False Discovery Rate LASSO Least Absolute Shrinkage and Selection Operator K-M Kaplan-Meier ROC Receiver Operating Characteristic AUC Area Under the Curve ssGSEA Single-Sample Gene Set Enrichment Analysis TILs Tumor-Infiltrating Lymphocytes GSVA Gene Set Variation Analysis aDCs Antigen-presenting Dendritic Cells iDCs Immature Dendritic Cells Tfh T Follicular Helper CCR Cytokine-cytokine Receptor Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou (KY2023-630-01). All procedures were performed in accordance with relevant institutional guidelines and regulations. Informed consent was obtained from all patients involved. Consent for publication Not applicable. Availability of data and materials The datasets analysed during the current study are available in TCGA (https://portal.gdc.cancer.gov/) and ICGC (https://dcc.icgc.org/projects/OV-AU) repositories. The datasets generated during the current study for mIF validation are not publicly available due to the privacy of patients but are available from the corresponding author on reasonable request. Competing interests The authors have declared that there are no competing interests. Funding This article was supported by the National Natural Science Foundation of China (82103647; 82272850), the Guangzhou Municipal Science and Technology Project (202201010899), National Health Commission Foundation of China (WKZX2023CX130001); Basic and Applied Basic Research Foundation of Guangdong Province (2024A1515010596); the PARP Inhibitor Oncology Research Foundation (Phase IV) of Chinese Anti-Cancer Association (CETSDHRCORP252-4-026). Author contributions All authors participated in this research, including conception and design (LZD, ZCF, HSY), data acquisition (CHX, CJ, HST, HJM), data analysis and interpretation (LZD, CHX, ZCF), material support (CJ, HST, HJM), study supervision (ZCF, HSY) and drafting the article or critically revising (LZD, CHX, CJ, ZCF, HSY). All authors read and approved the final manuscript. Acknowledgements We appreciate all the patients who participated in the study. References Konstantinopoulos PA, Matulonis UA. Clinical and translational advances in ovarian cancer therapy. Nat Cancer. 2023;4(9):1239-57. Lisio MA, Fu L, Goyeneche A, Gao ZH, Telleria C. High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints. Int J Mol Sci. 2019;20(4). Yan L, Chen Z. A Unifying Mechanism of DNA Translocation Underlying Chromatin Remodeling. Trends Biochem Sci. 2020;45(3):217-27. Lu J, Xu J, Li J, Pan T, Bai J, Wang L, et al. FACER: comprehensive molecular and functional characterization of epigenetic chromatin regulators. Nucleic Acids Res. 2018;46(19):10019-33. Plass C, Pfister SM, Lindroth AM, Bogatyrova O, Claus R, Lichter P. Mutations in regulators of the epigenome and their connections to global chromatin patterns in cancer. Nat Rev Genet. 2013;14(11):765-80. Masliah-Planchon J, Bièche I, Guinebretière JM, Bourdeaut F, Delattre O. SWI/SNF chromatin remodeling and human malignancies. Annu Rev Pathol. 2015;10:145-71. Kadoch C. Diverse compositions and functions of chromatin remodeling machines in cancer. Sci Transl Med. 2019;11(501). Zhao S, Allis CD, Wang GG. The language of chromatin modification in human cancers. Nat Rev Cancer. 2021;21(7):413-30. Yang C, Yu T, Lin Q. A signature based on chromatin regulation and tumor microenvironment infiltration in clear cell renal cell carcinoma. Epigenomics. 2022;14(17):995-1013. Griffin GK, Wu J, Iracheta-Vellve A, Patti JC, Hsu J, Davis T, et al. Epigenetic silencing by SETDB1 suppresses tumour intrinsic immunogenicity. Nature. 2021;595(7866):309-14. Sheng W, LaFleur MW, Nguyen TH, Chen S, Chakravarthy A, Conway JR, et al. LSD1 Ablation Stimulates Anti-tumor Immunity and Enables Checkpoint Blockade. Cell. 2018;174(3):549-63.e19. Lin J, Guo D, Liu H, Zhou W, Wang C, Müller I, et al. The SETDB1-TRIM28 Complex Suppresses Antitumor Immunity. Cancer Immunol Res. 2021;9(12):1413-24. Palucka K, Banchereau J. Cancer immunotherapy via dendritic cells. Nat Rev Cancer. 2012;12(4):265-77. Wculek SK, Cueto FJ, Mujal AM, Melero I, Krummel MF, Sancho D. Dendritic cells in cancer immunology and immunotherapy. Nat Rev Immunol. 2020;20(1):7-24. Swiecki M, Colonna M. The multifaceted biology of plasmacytoid dendritic cells. Nat Rev Immunol. 2015;15(8):471-85. Monti M, Ferrari G, Gazzurelli L, Bugatti M, Facchetti F, Vermi W. Plasmacytoid dendritic cells at the forefront of anti-cancer immunity: rewiring strategies for tumor microenvironment remodeling. J Exp Clin Cancer Res. 2024;43(1):196. Murthy HS, Zhang MJ, Chen K, Ahmed S, Deotare U, Ganguly S, et al. Allogeneic hematopoietic cell transplantation for blastic plasmacytoid dendritic cell neoplasm: a CIBMTR analysis. Blood Adv. 2023;7(22):7007-16. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315-6. Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the eye. Lancet Oncol. 2015;16(4):e173-80. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128-38. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017;77(21):e108-e10. Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35(20):4200-2. Győrffy B. Transcriptome-level discovery of survival-associated biomarkers and therapy targets in non-small-cell lung cancer. Br J Pharmacol. 2024;181(3):362-74. Győrffy B. Integrated analysis of public datasets for the discovery and validation of survival-associated genes in solid tumors. Innovation (Camb). 2024;5(3):100625. Tiberio L, Del Prete A, Schioppa T, Sozio F, Bosisio D, Sozzani S. Chemokine and chemotactic signals in dendritic cell migration. Cell Mol Immunol. 2018;15(4):346-52. Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A, et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368(22):2059-74. Alizadeh AA, Aranda V, Bardelli A, Blanpain C, Bock C, Borowski C, et al. Toward understanding and exploiting tumor heterogeneity. Nat Med. 2015;21(8):846-53. Sharma SV, Lee DY, Li B, Quinlan MP, Takahashi F, Maheswaran S, et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell. 2010;141(1):69-80. Wu J, Zhou Z, Li J, Liu H, Zhang H, Zhang J, et al. CHD4 promotes acquired chemoresistance and tumor progression by activating the MEK/ERK axis. Drug Resist Updat. 2023;66:100913. Wang Y, Chen Y, Bao L, Zhang B, Wang JE, Kumar A, et al. CHD4 Promotes Breast Cancer Progression as a Coactivator of Hypoxia-Inducible Factors. Cancer Res. 2020;80(18):3880-91. Gallo M, Coutinho FJ, Vanner RJ, Gayden T, Mack SC, Murison A, et al. MLL5 Orchestrates a Cancer Self-Renewal State by Repressing the Histone Variant H3.3 and Globally Reorganizing Chromatin. Cancer Cell. 2015;28(6):715-29. Damm F, Oberacker T, Thol F, Surdziel E, Wagner K, Chaturvedi A, et al. Prognostic importance of histone methyltransferase MLL5 expression in acute myeloid leukemia. J Clin Oncol. 2011;29(6):682-9. Liu L, Lin B, Yin S, Ball LE, Delaney JR, Long DT, et al. Arginine methylation of BRD4 by PRMT2/4 governs transcription and DNA repair. Sci Adv. 2022;8(49):eadd8928. Wu SY, Lai HT, Sanjib Banerjee N, Ma Z, Santana JF, Wei S, et al. IDR-targeting compounds suppress HPV genome replication via disruption of phospho-BRD4 association with DNA damage response factors. Mol Cell. 2024;84(2):202-20.e15. Ladang A, Rapino F, Heukamp LC, Tharun L, Shostak K, Hermand D, et al. Elp3 drives Wnt-dependent tumor initiation and regeneration in the intestine. J Exp Med. 2015;212(12):2057-75. Rosu A, El Hachem N, Rapino F, Rouault-Pierre K, Jorssen J, Somja J, et al. Loss of tRNA-modifying enzyme Elp3 activates a p53-dependent antitumor checkpoint in hematopoiesis. J Exp Med. 2021;218(3). Arruabarrena-Aristorena A, Maag JLV, Kittane S, Cai Y, Karthaus WR, Ladewig E, et al. FOXA1 Mutations Reveal Distinct Chromatin Profiles and Influence Therapeutic Response in Breast Cancer. Cancer Cell. 2020;38(4):534-50.e9. Gao S, Chen S, Han D, Wang Z, Li M, Han W, et al. Chromatin binding of FOXA1 is promoted by LSD1-mediated demethylation in prostate cancer. Nat Genet. 2020;52(10):1011-7. Zhang H, Zheng Y, Wang Z, Dong L, Xue L, Tian X, et al. KLF12 interacts with TRIM27 to affect cisplatin resistance and cancer metastasis in esophageal squamous cell carcinoma by regulating L1CAM expression. Drug Resist Updat. 2024;76:101096. Yang Y, Zhu Y, Zhou S, Tang P, Xu R, Zhang Y, et al. TRIM27 cooperates with STK38L to inhibit ULK1-mediated autophagy and promote tumorigenesis. Embo j. 2022;41(14):e109777. Dhalluin C, Carlson JE, Zeng L, He C, Aggarwal AK, Zhou MM. Structure and ligand of a histone acetyltransferase bromodomain. Nature. 1999;399(6735):491-6. Wu SY, Chiang CM. The double bromodomain-containing chromatin adaptor Brd4 and transcriptional regulation. J Biol Chem. 2007;282(18):13141-5. Li X, Fu Y, Yang B, Guo E, Wu Y, Huang J, et al. BRD4 Inhibition by AZD5153 Promotes Antitumor Immunity via Depolarizing M2 Macrophages. Front Immunol. 2020;11:89. Li L, Gao L, Zhou H, Shi C, Zhang X, Zhang D, et al. High Expression Level of BRD4 Is Associated with a Poor Prognosis and Immune Infiltration in Esophageal Squamous Cell Carcinoma. Dig Dis Sci. 2023;68(7):2997-3008. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 11 Feb, 2026 Read the published version in Journal of Ovarian Research → Version 1 posted Editorial decision: Revision requested 25 Oct, 2025 Reviews received at journal 25 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers invited by journal 14 Jul, 2025 Editor assigned by journal 09 Jul, 2025 Submission checks completed at journal 09 Jul, 2025 First submitted to journal 02 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7030302","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485376424,"identity":"02e19321-622a-4d34-9c2a-9ddaeb63a1e1","order_by":0,"name":"Zidan Lin","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zidan","middleName":"","lastName":"Lin","suffix":""},{"id":485376427,"identity":"7783879a-8979-409c-bc37-77315e8c25ca","order_by":1,"name":"Hongxi Chen","email":"","orcid":"","institution":"Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hongxi","middleName":"","lastName":"Chen","suffix":""},{"id":485376428,"identity":"b6e12f16-2809-4f9f-a497-270767535272","order_by":2,"name":"Jing Chen","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Chen","suffix":""},{"id":485376430,"identity":"72f1a2aa-df60-4a7a-bcb3-1ec77f7d24a0","order_by":3,"name":"Shuting Huang","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical 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He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACAwYGNiiT+QADD8MBkrSwJZCshceAOC3m7O3PHvzccVjenH/N5xdvd9xh4Jc+foHh5w7cWix7zpgb9p45bLhzxtttlnPPPGOQ7MspYOw9g8dhN3LYJHjbDjNuuHF2mzGQwWBwhieBmbENj5b7z59J/m07bL/hxplnRGq5wWAmDVSZuOF8D/NjiBb2A/i1nMkxk5ZtS0/ecIPNjHHumcM8kj08DAd78Wk5fvyZ5Ns2a9sN5w8//vB2x2E5fh72hw9+4tECBc0MDBIJbBKMDcCoAUbQAYIaGBjqGBj4DzB/AGoBAvYHROgYBaNgFIyCEQQAbpVcwqea38oAAAAASUVORK5CYII=","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shanyang","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-07-02 14:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7030302/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7030302/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13048-026-02022-z","type":"published","date":"2026-02-11T15:58:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87042505,"identity":"b20bd4a6-1651-4761-a7af-d6299130b818","added_by":"auto","created_at":"2025-07-18 14:11:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":365087,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow diagram of data collection and analytical procedures.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7030302/v1/5384f1146ce74ad939f61d92.png"},{"id":87042506,"identity":"88ad9431-deee-405a-8b99-3c90347cad82","added_by":"auto","created_at":"2025-07-18 14:11:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1180178,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots depicting the results of univariate (A) and multivariate (B) Cox regression analyses evaluating the association between gene expression and overall survival (OS).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7030302/v1/687c0a3789b05cc6a8e99039.png"},{"id":87042507,"identity":"8d7c1f7f-e99f-47f1-b477-d0898b087ea8","added_by":"auto","created_at":"2025-07-18 14:11:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1037379,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic assessment of the 11-gene signature in the TCGA (A, B) and ICGC (C, D) cohorts. (A, C) Kaplan–Meier curves illustrating OS differences between high-risk and low-risk groups in the TCGA (A) and ICGC (C) cohorts. (B, D) Time-dependent ROC curves validating the prognostic performance of the risk score in the TCGA (B) and ICGC (D) cohorts.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7030302/v1/1c78b8df9547226e4df82a36.png"},{"id":87042504,"identity":"a8967b20-ccf2-4179-9601-1ad95eafd74c","added_by":"auto","created_at":"2025-07-18 14:11:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1555532,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate and multivariate Cox regression analyses for OS prediction and construction of a prognostic nomogram. (A, C) Univariate Cox regression of risk score and clinicopathological characteristics in the TCGA (A) and ICGC (C) cohorts. (B, D) Multivariate Cox regression of risk score and clinicopathological characteristics in the TCGA (B) and ICGC (D) cohorts. (E) Nomogram predicting 1-, 3-, and 5-year OS in patients with ovarian cancer. (F) Calibration curves assessing the predictive accuracy of the nomogram for 1-, 3-, and 5-year OS.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7030302/v1/cb5595394c04a9b8c788e58d.png"},{"id":87043614,"identity":"2c50149e-495c-4f32-a64c-e0457167575d","added_by":"auto","created_at":"2025-07-18 14:19:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1200837,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of immune landscape between high- and low-risk groups in the training and validation cohorts using ssGSEA. (A, B) Boxplots showing the scores of 16 infiltrating immune cell types. (C, D) Boxplots displaying the activity scores of 13 immune-related functions. Tfh, T follicular helper cells; TIL, tumor-infiltrating lymphocytes; CCR, cytokine–cytokine receptor interaction. Statistical significance is indicated as follows: ns, not significant; *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7030302/v1/cade6173c259fd0bd156c971.png"},{"id":87042512,"identity":"ae57175a-b729-47cc-ae1d-f62139aee034","added_by":"auto","created_at":"2025-07-18 14:11:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4008234,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of BRD4 as a key gene associated with plasmacytoid dendritic cell (pDC) infiltration. (A) Correlation analysis between dendritic cell abundance and CRRG expression via the TIMER database. (B) Analysis of immune cell infiltration in relation to BRD4 expression using the TISIDB database. (C) Comparison of ssGSEA scores between BRD4-high and BRD4-low subgroups in the TCGA and ICGC cohorts. (D) Kaplan–Meier survival curves evaluating OS based on BRD4 and CLEC4C expression levels.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7030302/v1/20a09e6ebe68cfa97dba897e.png"},{"id":87042510,"identity":"c9c05c59-cb1f-4ebc-8319-2ab7c065e79d","added_by":"auto","created_at":"2025-07-18 14:11:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":5816081,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between BRD4 expression and pDC infiltration in clinical ovarian cancer specimens. (A) Representative multiplex immunofluorescence (mIF) images showing BRD4 and BDCA2 expression in ovarian cancer tissues; scale bar = 50 μm. (B) Quantification of BDCA2⁺ pDCs per field in BRD4-low versus BRD4-high tissue sections. (C) Correlation analysis between BRD4 expression and BDCA2⁺ pDC density. (D) Kaplan–Meier survival curves comparing OS between BRD4\u003csup\u003ehigh\u003c/sup\u003eBDCA2\u003csup\u003elow\u003c/sup\u003e and BRD4\u003csup\u003elow\u003c/sup\u003eBDCA2\u003csup\u003ehigh\u003c/sup\u003e patient groups. ****P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7030302/v1/9e298f020f18558e9db07f1b.png"},{"id":87043615,"identity":"2dad41eb-1c50-4249-aba8-6335674f1114","added_by":"auto","created_at":"2025-07-18 14:19:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4466935,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of chemokines associated with BRD4 expression and pDC infiltration. (A) Venn diagram showing overlap of chemokines correlated with BRD4 expression across the TISIDB database and the TCGA and ICGC cohorts. (B) The five overlapping chemokines are negatively correlated with BRD4 expression in the TCGA cohort. (C) All five chemokines are positively correlated with pDC infiltration in ovarian cancer, as identified through the TISIDB database.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7030302/v1/84972ad1e850ac42a5d865f9.png"},{"id":102785486,"identity":"2e50434a-23c8-41eb-9bce-fa9e3a70726d","added_by":"auto","created_at":"2026-02-16 16:07:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18402040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7030302/v1/8d0daba3-f42d-4252-8a6d-987de95af910.pdf"},{"id":87042508,"identity":"4c8de59b-54c5-4de6-8804-68d0ff8e20fb","added_by":"auto","created_at":"2025-07-18 14:11:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1185438,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7030302/v1/85ee8faceb4515ef0d62b099.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"BRD4-Associated Chromatin Remodeling Signature Links Epigenetic Regulation to Immune Landscape in Ovarian Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer (OV) remains one of the most lethal gynecologic malignancies worldwide, largely due to its insidious onset, high rate of metastasis, and frequent recurrence after initial therapy [1]. Despite advances in surgery and chemotherapy, the overall prognosis for patients with advanced-stage OV remains poor, with a 5-year survival rate below 50% [2]. Therefore, identifying reliable prognostic biomarkers and uncovering the biological mechanisms underlying immune evasion and disease progression are crucial for improving patient stratification and therapeutic outcomes.\u003c/p\u003e\n\u003cp\u003eChromatin remodeling-related genes (CRRGs) play critical roles in regulating gene expression, maintaining genomic stability [3], and orchestrating cellular responses to environmental signals, including immune stimuli [4, 5]. Emerging evidence suggests that dysregulation of chromatin remodeling is closely linked to cancer development [6] [7], progression [8], and the shaping of the tumor immune microenvironment (TIME) [9-12]. However, the prognostic value of CRRGs in ovarian cancer and their potential involvement in modulating anti-tumor immunity remain largely unexplored.\u003c/p\u003e\n\u003cp\u003eDendritic cells (DCs), particularly plasmacytoid dendritic cells (pDCs), are central players in anti-tumor immunity due to their ability to present antigens and initiate T cell responses [13-15]. Decreased infiltration or functional impairment of pDCs in tumors has been associated with immune escape and poor clinical outcomes [16, 17]. Nevertheless, the regulatory factors and molecular pathways affecting pDC infiltration in OV are not well defined.\u003c/p\u003e\n\u003cp\u003eIn this study, we aimed to construct an immune-associated prognostic signature based on differentially expressed CRRGs and to explore its relevance to the immune landscape of ovarian cancer. By integrating transcriptomic data from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), we developed and validated an 11-gene CRRG-based prognostic model. We further identified BRD4 as a key gene negatively associated with pDC infiltration and uncovered a potential BRD4\u0026ndash;chemokine\u0026ndash;pDC regulatory axis that may contribute to immune suppression and adverse prognosis in OV. These findings offer novel insights into the epigenetic regulation of the tumor immune microenvironment and provide potential targets for prognostic assessment and immunotherapy in ovarian cancer.\u003c/p\u003e\n"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Guangdong Provincial People\u0026rsquo;s Hospital, Guangdong Academy of Medical Sciences, Guangzhou (KY2023-630-01). All procedures were performed in accordance with relevant institutional guidelines and regulations. Informed consent was obtained from all patients involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA sequencing (RNA-seq) data and corresponding clinical information for 376 ovarian cancer (OV) samples were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). Immune scores for the TCGA OV samples were retrieved from the ESTIMATE database (https://bioinformatics.mdanderson.org/estimate/disease/). After excluding samples lacking immune score data, a total of 228 TCGA OV samples were included as the training cohort. An external validation cohort comprising RNA-seq data and clinical information from 111 OV samples was downloaded from the International Cancer Genome Consortium (ICGC) (https://dcc.icgc.org/projects/OV-AU). Both TCGA and ICGC datasets are publicly available and were used in accordance with their respective data access policies and publication guidelines. Additionally, 126 patients with histologically confirmed ovarian cancer treated at Guangdong Provincial People\u0026rsquo;s Hospital were enrolled for multiplex immunofluorescence (mIF) validation, with written informed consent obtained from all participants.\u003c/p\u003e\n\u003cp\u003eA total of 870 chromatin remodeling-related genes (CRRGs) were sourced from a previously published authoritative study (Supplementary Table S1) [4].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and Validation of an Immune-Associated Prognostic Signature Based on CRRGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene expression data were normalized using the \u0026ldquo;limma\u0026rdquo; R package. TCGA OV patients were divided into high (n = 115) and low (n = 113) immune score groups based on the median immune score. Differentially expressed genes (DEGs) between these groups were identified using the \u0026ldquo;DESeq2\u0026rdquo; R package, applying the criteria of |log2 fold change| \u0026gt; 0 and false discovery rate (FDR) \u0026lt; 0.05. CRRGs with prognostic significance were identified via univariate Cox regression analysis.\u003c/p\u003e\n\u003cp\u003eTo avoid overfitting, a least absolute shrinkage and selection operator (LASSO) Cox regression model was applied using the \u0026ldquo;glmnet\u0026rdquo; R package. Tenfold cross-validation determined the optimal penalty parameter (\u0026lambda;) corresponding to the minimum partial likelihood deviance. The risk score for each patient was calculated using the following formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003en\u003c/em\u003e is the number of selected genes, \u003cem\u003ecoef(i)\u003c/em\u003e is the regression coefficient, and \u003cem\u003eExp(i)\u003c/em\u003e is the normalized expression level of gene \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003ePatients were classified into high- and low-risk groups based on the median risk score. Kaplan\u0026ndash;Meier (K-M) survival curves and log-rank tests were used to compare overall survival (OS) between risk groups. Optimal cutoff values for each gene were determined using the \u0026ldquo;surv_cutpoint\u0026rdquo; function from the \u0026ldquo;survminer\u0026rdquo; R package. The prognostic performance of the gene signature was assessed using time-dependent receiver operating characteristic (ROC) curves generated with the \u0026ldquo;survivalROC\u0026rdquo; R package [18]. The model was further validated in the ICGC cohort using identical statistical methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNomogram Construction and Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate and multivariate Cox regression analyses were conducted to determine whether the CRRG-based risk score and clinical variables served as independent prognostic factors. A nomogram integrating these factors was constructed to estimate 1-, 3-, and 5-year OS probabilities using the \u0026ldquo;rms\u0026rdquo; R package [19]. Calibration curves were generated to assess the predictive accuracy of the nomogram [20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Landscape and Pathway Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-sample gene set enrichment analysis (ssGSEA) was performed using the \u0026ldquo;GSVA\u0026rdquo; R package to quantify the infiltration scores of 16 immune cell types and the activity of 13 immune-related pathways. The annotated gene set used is detailed in Supplementary Table S2.\u003c/p\u003e\n\u003cp\u003eImmune cell infiltration was further analyzed using the TIMER database (https://cistrome.shinyapps.io/timer/) [21], which provides estimates for B cells, CD4⁺ and CD8⁺ T cells, macrophages, neutrophils, and dendritic cells. Associations between gene expression, tumor-infiltrating lymphocytes (TILs), and chemokines were examined via the TISIDB platform (http://cis.hku.hk/TISIDB/index.php) [22]. The relative abundance of TILs was inferred using gene set variation analysis (GSVA) based on the normalized gene expression profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kaplan\u0026ndash;Meier Plotter tool (https://kmplot.com/analysis/) [23, 24]\u0026nbsp;was used to explore associations between gene expression and OS in OV patients. Hazard ratios and log-rank P values were calculated to assess statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiplex Immunofluorescence (mIF) Staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments involving human tissues were conducted under ethical approval and followed standard protocols. Formalin-fixed paraffin-embedded (FFPE) tumor samples from 126 OV patients were processed using the Opal 7-Color IHC Kit (Akoya Biosciences, Cat. No. NEL811001KT). After deparaffinization and antigen retrieval in sodium citrate buffer, sections were incubated with antibodies against BRD4 (Abcam, ab128874) and BDCA2 (Abcam, ab239077), followed by DAPI nuclear staining. Imaging was performed using the Vectra Polaris Quantitative Pathology Imaging System, and quantitative analysis was conducted with Phenochart software (version 1.0.12; Akoya Biosciences).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using R software (v4.2.2), SPSS (v25.0), and GraphPad Prism (v9.5). Differences between two groups were assessed using either the Student\u0026rsquo;s t-test (parametric) or the Mann\u0026ndash;Whitney U test (non-parametric). Categorical variables were analyzed using Fisher\u0026rsquo;s exact test or the Chi-square test, as appropriate. Univariate and multivariate Cox regression analyses were applied to identify independent prognostic factors for OS. Correlations between variables were evaluated using Pearson\u0026rsquo;s or Spearman\u0026rsquo;s correlation coefficients. A two-tailed P-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eConstruction and Validation of an Immune-Associated Prognostic Signature Based on CRRGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe detailed workflow of this study is illustrated in Figure 1. A total of 228 ovarian cancer (OV) patients from the TCGA-OV cohort and 111 OV patients from the ICGC (OV-AU) cohort were included in the final analysis. The clinical characteristics of these cohorts are summarized in Supplementary Table S3.\u003c/p\u003e\n\u003cp\u003eAmong the chromatin remodeling-related genes (CRRGs), 399 out of 870 genes (45.9%) were found to be differentially expressed between the high and low immune score groups. Of these, 26 genes were significantly associated with overall survival (OS) in univariate Cox regression analysis (Figure 2A). To refine the prognostic model, least absolute shrinkage and selection operator (LASSO) Cox regression was applied to the expression profiles of these 26 genes. Subsequently, multivariate Cox regression analysis was performed, and the results are presented in a forest plot (Figure 2B).\u003c/p\u003e\n\u003cp\u003eAn 11-gene immune-associated prognostic signature was ultimately established based on the optimal \u0026lambda; value derived from LASSO analysis (Supplementary Figure 1A, B). The risk score was calculated using the following formula:\u003c/p\u003e\n\u003cp\u003eRisk score = (0.0031 \u0026times; \u003cem\u003eBRD4\u003c/em\u003e) + (0.0098 \u0026times; \u003cem\u003eCHD4\u003c/em\u003e) + (\u0026minus;0.0207 \u0026times; \u003cem\u003eELP3\u003c/em\u003e) + (0.0012 \u0026times; \u003cem\u003eFBL\u003c/em\u003e) + (\u0026minus;0.0029 \u0026times; \u003cem\u003eFOXA1\u003c/em\u003e) + (0.0052 \u0026times; \u003cem\u003eING4\u003c/em\u003e) + (0.0029 \u0026times; \u003cem\u003eKMT2E\u003c/em\u003e) + (\u0026minus;0.0050 \u0026times; \u003cem\u003eTADA1\u003c/em\u003e) + (0.0045 \u0026times; \u003cem\u003eTAF6\u003c/em\u003e) + (\u0026minus;0.0263 \u0026times; \u003cem\u003eTRIM27\u003c/em\u003e) + (\u0026minus;0.0100 \u0026times; \u003cem\u003eWDR77\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003eBased on the median risk score, patients from the TCGA cohort were stratified into high-risk (n = 113) and low-risk (n = 115) groups (Supplementary Figure S2A). Kaplan\u0026ndash;Meier survival analysis demonstrated that patients in the high-risk group had significantly poorer overall survival compared to those in the low-risk group (P \u0026lt; 0.0001, Figure 3A). Time-dependent receiver operating characteristic (ROC) curve analysis revealed that the risk score exhibited favorable predictive performance, with area under the curve (AUC) values of 0.748 at 3 years and 0.793 at 5 years (Figure 3B).\u003c/p\u003e\n\u003cp\u003eTo assess the robustness and generalizability of the prognostic signature, the same formula derived from the TCGA cohort was applied to the ICGC (OV-AU) cohort. Patients were classified into high-risk (n = 55) and low-risk (n = 56) groups based on the median risk score (Supplementary Figure S2B). Consistent with the TCGA findings, patients in the high-risk group exhibited significantly shorter survival durations compared to those in the low-risk group (\u003cem\u003eP\u003c/em\u003e = 0.0049, Figure 3C). The AUC values for the 11-gene signature in the ICGC cohort were 0.717 at 3 years and 0.659 at 5 years (Figure 3D), further supporting its prognostic utility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent Prognostic Analysis of the Signature and Construction of the Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate and multivariate Cox regression analyses were conducted to determine whether common clinicopathological variables and the risk score served as independent prognostic predictors for overall survival (OS). In the univariate analysis, age (HR = 1.4379, 95% CI = 1.0083\u0026ndash;2.0505, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and clinical stage (HR = 2.7193, 95% CI = 1.0021\u0026ndash;7.3790, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) were significantly associated with OS in the TCGA cohort (Fig. 4A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, the risk score showed a strong correlation with OS in both the TCGA (HR = 3.2865, 95% CI = 2.2537\u0026ndash;4.7925, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and ICGC (HR = 1.8289, 95% CI = 1.1908\u0026ndash;2.8091, \u003cem\u003eP\u003c/em\u003e = 0.0058) cohorts (Fig. 4A, C). Upon adjusting for other confounding factors, age remained an independent prognostic factor in the TCGA cohort (HR = 1.5412, 95% CI = 1.0775\u0026ndash;2.2042, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), and the risk score retained its independent prognostic value in multivariate analyses for both cohorts (TCGA: HR = 3.2808, 95% CI = 2.2379\u0026ndash;4.8098, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ICGC: HR = 1.8243, 95% CI = 1.1877\u0026ndash;2.8021, \u003cem\u003eP\u003c/em\u003e = 0.006; Fig. 4B, D). These results indicate that both clinicopathological parameters and the CRRG-based prognostic signature independently contribute to survival prediction in ovarian cancer (OV).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo provide individualized survival predictions, we developed a nomogram incorporating clinicopathological variables and the risk score to estimate 1-, 3-, and 5-year OS based on the TCGA cohort (Fig. 4E). The calibration curve demonstrated good agreement between predicted and observed survival outcomes (Fig. 4F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Status Analysis in the TCGA and ICGC Cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the relationship between immune status and the risk score, we performed single-sample gene set enrichment analysis (ssGSEA) to quantify immune cell infiltration and functional activity. Notably, the enrichment scores for antigen-presenting dendritic cells (aDCs), immature dendritic cells (iDCs), and plasmacytoid dendritic cells (pDCs) significantly differed between the high- and low-risk groups in the ICGC cohort (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig. 5B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecifically, pDC scores were significantly reduced in the high-risk group in both the TCGA and ICGC cohorts (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig. 5A, B). Additionally, antigen presentation-related pathways\u0026mdash;APC costimulation, HLA expression, and MHC class I signaling\u0026mdash;were downregulated in the high-risk group of the TCGA cohort (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig. 5C). These findings were supported by similar trends in the ICGC cohort, where T-cell coinhibition scores were reduced and type II IFN response scores were elevated in the high-risk group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig. 5D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of a Key Gene Associated with pDC Infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify key CRRGs related to dendritic cell infiltration, we employed the TIMER database to assess correlations between the 11-gene signature and dendritic cell levels. BRD4, FBL, CHD4, MLL5 (KMT2E), TADA1, and TAF6 were negatively correlated with dendritic cell infiltration (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig. 6A), with BRD4 showing the strongest inverse correlation. Further analysis using the TISIDB database confirmed a significant negative correlation between BRD4 expression and pDC infiltration (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig. 6B). Based on RNA-seq data from the TCGA and ICGC cohorts, patients were stratified into BRD4-high and BRD4-low groups using the median expression level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsistent with previous findings, ssGSEA analysis showed that pDC infiltration was significantly lower in BRD4-high patients in both cohorts (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig. 6C). Kaplan\u0026ndash;Meier survival analysis revealed that high BRD4 expression was associated with poorer OS (HR = 1.38, 95% CI = 1.04\u0026ndash;1.85, \u003cem\u003eP\u003c/em\u003e = 0.028), whereas high expression of CLEC4C (encoding BDCA2, a marker of pDCs) was positively associated with OS (HR = 0.71, 95% CI = 0.55\u0026ndash;0.93, \u003cem\u003eP\u003c/em\u003e = 0.011; Fig. 6D).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of the Correlation Between BRD4 Expression and pDC Infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo experimentally validate the association between BRD4 expression and pDC infiltration, we analyzed tumor samples from 126 OV patients using multiplex immunofluorescence (mIF) staining targeting BRD4 and BDCA2. Patients were stratified into BRD4-high and BRD4-low groups based on fluorescence intensity. The BRD4-high group showed more advanced FIGO stages (III\u0026ndash;IV), higher lymph node and distant metastases, and elevated CA125 levels, though no differences were found in age, histological type, or grade (Supplementary Table S4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emIF confirmed that BDCA2\u003csup\u003e+\u003c/sup\u003e pDC infiltration was significantly lower in the BRD4-high group (Fig. 7A, B). A significant negative correlation was observed between BRD4 expression and BDCA2\u003csup\u003e+\u003c/sup\u003e pDC infiltration (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001, \u003cem\u003er\u003c/em\u003e = \u0026ndash;0.514, Pearson\u0026apos;s correlation; Fig. 7C), consistent with in silico findings. Moreover, patients with the BRD4\u003csup\u003elow\u003c/sup\u003eBDCA2\u003csup\u003ehigh\u003c/sup\u003e phenotype exhibited significantly prolonged OS compared to those with the BRD4\u003csup\u003ehigh\u003c/sup\u003eBDCA2\u003csup\u003elow\u003c/sup\u003e (\u003cem\u003eP\u003c/em\u003e = 0.0049; Fig. 7D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of the BRD4\u0026ndash;Chemokine\u0026ndash;pDC Axis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe infiltration of pDCs into the tumor microenvironment is often regulated by chemokines [25]. To elucidate the mechanistic role of BRD4 in modulating pDC recruitment, we explored its association with chemokine expression using the TISIDB database (Supplementary Fig. S3A). RNA-seq data from both the TCGA and ICGC cohorts were also analyzed. Consistent inverse correlations between BRD4 expression and five chemokines\u0026mdash;CCL11, CCL17, CCL19, CXCL13, and XCL2\u0026mdash;were observed across datasets (Fig. 8A, B; Supplementary Fig. S3B, C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, TISIDB analysis revealed that infiltration levels of pDCs were positively correlated with the expression of these five chemokines (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001 for all; Fig. 8C). These findings suggest that BRD4 may suppress pDC infiltration by downregulating key pDC-attracting chemokines, providing a mechanistic explanation for the observed inverse relationship between BRD4 expression and pDC presence in the OV tumor microenvironment.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed and validated an immune-associated prognostic signature based on chromatin remodeling-related genes (CRRGs) in ovarian cancer. By integrating transcriptomic and clinical data from two independent cohorts (TCGA and ICGC), we identified an 11-gene signature that effectively stratified patients into high- and low-risk groups with significantly different overall survival outcomes. The signature remained an independent prognostic factor after adjustment for conventional clinical variables, and its predictive performance was confirmed in both cohorts. Importantly, we uncovered a novel link between BRD4 expression, reduced plasmacytoid dendritic cell (pDC) infiltration, and poor prognosis, suggesting a potential epigenetic\u0026ndash;immune axis in the ovarian tumor microenvironment.\u003c/p\u003e\n\u003cp\u003eChromatin remodeling has been increasingly recognized as a central regulatory mechanism in cancer, not only by altering transcriptional landscapes but also by influencing immune surveillance [26-28]. Among the 11 genes constituting our prognostic model, several (e.g., CHD4, KMT2E, BRD4) are well-established chromatin regulators implicated in oncogenic transcription, DNA repair, and cell cycle control [29-34]. Interestingly, some genes (e.g., ELP3, FOXA1, TRIM27) demonstrated protective roles [35-40], underscoring the complex and context-dependent functions of CRRGs in tumor biology. The application of LASSO and multivariate Cox regression ensured that the model captured the most informative and non-redundant prognostic features.\u003c/p\u003e\n\u003cp\u003eA key finding of our study is the identification of BRD4 as a potential suppressor of pDC infiltration in the ovarian cancer microenvironment. BRD4, a member of the bromodomain and extra-terminal (BET) family, is known to regulate transcription of oncogenes and inflammatory mediators via binding to acetylated histones [41, 42]. We observed that high BRD4 expression correlated negatively with pDC infiltration, as determined by both in silico (ssGSEA, TIMER, TISIDB) and experimental validation using multiplex immunofluorescence in clinical samples. Notably, patients with high BRD4 and low BDCA2\u003csup\u003e+\u003c/sup\u003e pDC levels exhibited the worst overall survival, highlighting the clinical relevance of this axis.\u003c/p\u003e\n\u003cp\u003eMechanistically, we propose that BRD4 may impair pDC recruitment by downregulating a specific subset of chemokines, including CCL11, CCL17, CCL19, CXCL13, and XCL2. These chemokines are known to mediate the recruitment and positioning of dendritic cells and other immune effectors within the tumor. The inverse correlation between BRD4 and these chemokines was consistently observed across databases and cohorts. Although the precise regulatory circuitry remains to be fully elucidated, it is plausible that BRD4, through transcriptional repression or chromatin compaction, directly or indirectly limits the expression of these chemokines, thereby contributing to an immunosuppressive microenvironment.\u003c/p\u003e\n\u003cp\u003eOur findings add to the growing body of literature highlighting the interplay between epigenetic regulators and tumor immunity. Previous studies have shown that BET inhibitors can restore anti-tumor immunity by reprogramming tumor-associated macrophages, enhancing T cell infiltration, or modulating interferon responses [43] [44]. Our study extends this concept by implicating BRD4 in the regulation of pDC infiltration\u0026mdash;a relatively underexplored cell type in ovarian cancer\u0026mdash;thus providing a rationale for further exploration of BRD4 as an immunomodulatory target.\u003c/p\u003e\n\u003cp\u003eClinically, the CRRG-based signature and the BRD4\u0026ndash;chemokine\u0026ndash;pDC axis offer promising avenues for patient stratification and therapeutic intervention. The gene signature could potentially serve as a prognostic tool to identify high-risk patients who may benefit from closer surveillance or novel therapeutic strategies. Meanwhile, targeting BRD4 may not only suppress tumor proliferation but also reshape the immune microenvironment by restoring pDC recruitment and function. Combining BET inhibitors with immune checkpoint blockade or dendritic cell-based vaccines could be a promising strategy worth preclinical and clinical investigation.\u003c/p\u003e\n\u003cp\u003eHowever, several limitations of this study should be acknowledged. First, although our model was validated in an external cohort and supported by mIF data, its predictive utility should be further tested in prospective, multi-center clinical studies. Second, the regulatory relationship between BRD4 and chemokine expression was inferred through correlation analysis and remains to be mechanistically verified using functional experiments such as chromatin immunoprecipitation (ChIP) or BRD4 knockdown assays. Third, while we focused on pDCs due to their immunoregulatory roles and clinical relevance, the broader impact of CRRGs on other immune cell types and stromal interactions warrants further exploration.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study presents a novel CRRG-based prognostic signature for ovarian cancer and identifies BRD4 as a key epigenetic regulator linked to impaired pDC infiltration and unfavorable prognosis. These findings underscore the importance of chromatin remodeling in shaping the immune landscape of ovarian tumors and highlight the potential of targeting epigenetic-immune pathways for therapeutic benefit.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003eOV Ovarian Cancer\u003c/p\u003e\n\u003cp\u003eCRRGs Chromatin Remodeling-Related Genes\u003c/p\u003e\n\u003cp\u003eTIME Tumor Immune Microenvironment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003epDCs Plasmacytoid Dendritic Cells\u003c/p\u003e\n\u003cp\u003eDCs Dendritic Cells\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTCGA The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eICGC International Cancer Genome Consortium\u003c/p\u003e\n\u003cp\u003eOS Overall Survival\u003c/p\u003e\n\u003cp\u003eMIF Multiplex Immunofluorescence\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRNA-seq RNA Sequencing\u003c/p\u003e\n\u003cp\u003eDEGs Differentially Expressed Genes\u003c/p\u003e\n\u003cp\u003eFDR False Discovery Rate\u003c/p\u003e\n\u003cp\u003eLASSO Least Absolute Shrinkage and Selection Operator\u003c/p\u003e\n\u003cp\u003eK-M Kaplan-Meier\u003c/p\u003e\n\u003cp\u003eROC Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003eAUC Area Under the Curve\u003c/p\u003e\n\u003cp\u003essGSEA Single-Sample Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eTILs Tumor-Infiltrating Lymphocytes\u003c/p\u003e\n\u003cp\u003eGSVA Gene Set Variation Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eaDCs Antigen-presenting Dendritic Cells\u003c/p\u003e\n\u003cp\u003eiDCs Immature Dendritic Cells\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTfh T Follicular Helper\u003c/p\u003e\n\u003cp\u003eCCR Cytokine-cytokine Receptor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Guangdong Provincial People\u0026rsquo;s Hospital, Guangdong Academy of Medical Sciences, Guangzhou (KY2023-630-01). All procedures were performed in accordance with relevant institutional guidelines and regulations. Informed consent was obtained from all patients involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available in TCGA (https://portal.gdc.cancer.gov/) and ICGC (https://dcc.icgc.org/projects/OV-AU) repositories. The datasets generated during the current study for mIF validation are not publicly available due to the privacy of patients but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article was supported by the National Natural Science Foundation of China (82103647; 82272850), the Guangzhou Municipal Science and Technology Project (202201010899), National Health Commission Foundation of China (WKZX2023CX130001); Basic and Applied Basic Research Foundation of Guangdong Province (2024A1515010596); the PARP Inhibitor Oncology Research Foundation (Phase IV) of Chinese Anti-Cancer Association (CETSDHRCORP252-4-026).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors participated in this research, including conception and design (LZD, ZCF, HSY), data acquisition (CHX, CJ, HST, HJM), data analysis and interpretation (LZD, CHX, ZCF), material support (CJ, HST, HJM), study supervision (ZCF, HSY) and drafting the article or critically revising (LZD, CHX, CJ, ZCF, HSY). All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate all the patients who participated in the study.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKonstantinopoulos PA, Matulonis UA. Clinical and translational advances in ovarian cancer therapy. Nat Cancer. 2023;4(9):1239-57.\u003c/li\u003e\n\u003cli\u003eLisio MA, Fu L, Goyeneche A, Gao ZH, Telleria C. High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints. Int J Mol Sci. 2019;20(4).\u003c/li\u003e\n\u003cli\u003eYan L, Chen Z. A Unifying Mechanism of DNA Translocation Underlying Chromatin Remodeling. Trends Biochem Sci. 2020;45(3):217-27.\u003c/li\u003e\n\u003cli\u003eLu J, Xu J, Li J, Pan T, Bai J, Wang L, et al. FACER: comprehensive molecular and functional characterization of epigenetic chromatin regulators. Nucleic Acids Res. 2018;46(19):10019-33.\u003c/li\u003e\n\u003cli\u003ePlass C, Pfister SM, Lindroth AM, Bogatyrova O, Claus R, Lichter P. Mutations in regulators of the epigenome and their connections to global chromatin patterns in cancer. Nat Rev Genet. 2013;14(11):765-80.\u003c/li\u003e\n\u003cli\u003eMasliah-Planchon J, Bi\u0026egrave;che I, Guinebreti\u0026egrave;re JM, Bourdeaut F, Delattre O. SWI/SNF chromatin remodeling and human malignancies. Annu Rev Pathol. 2015;10:145-71.\u003c/li\u003e\n\u003cli\u003eKadoch C. Diverse compositions and functions of chromatin remodeling machines in cancer. Sci Transl Med. 2019;11(501).\u003c/li\u003e\n\u003cli\u003eZhao S, Allis CD, Wang GG. The language of chromatin modification in human cancers. Nat Rev Cancer. 2021;21(7):413-30.\u003c/li\u003e\n\u003cli\u003eYang C, Yu T, Lin Q. A signature based on chromatin regulation and tumor microenvironment infiltration in clear cell renal cell carcinoma. Epigenomics. 2022;14(17):995-1013.\u003c/li\u003e\n\u003cli\u003eGriffin GK, Wu J, Iracheta-Vellve A, Patti JC, Hsu J, Davis T, et al. Epigenetic silencing by SETDB1 suppresses tumour intrinsic immunogenicity. Nature. 2021;595(7866):309-14.\u003c/li\u003e\n\u003cli\u003eSheng W, LaFleur MW, Nguyen TH, Chen S, Chakravarthy A, Conway JR, et al. LSD1 Ablation Stimulates Anti-tumor Immunity and Enables Checkpoint Blockade. Cell. 2018;174(3):549-63.e19.\u003c/li\u003e\n\u003cli\u003eLin J, Guo D, Liu H, Zhou W, Wang C, M\u0026uuml;ller I, et al. The SETDB1-TRIM28 Complex Suppresses Antitumor Immunity. Cancer Immunol Res. 2021;9(12):1413-24.\u003c/li\u003e\n\u003cli\u003ePalucka K, Banchereau J. Cancer immunotherapy via dendritic cells. Nat Rev Cancer. 2012;12(4):265-77.\u003c/li\u003e\n\u003cli\u003eWculek SK, Cueto FJ, Mujal AM, Melero I, Krummel MF, Sancho D. Dendritic cells in cancer immunology and immunotherapy. Nat Rev Immunol. 2020;20(1):7-24.\u003c/li\u003e\n\u003cli\u003eSwiecki M, Colonna M. The multifaceted biology of plasmacytoid dendritic cells. Nat Rev Immunol. 2015;15(8):471-85.\u003c/li\u003e\n\u003cli\u003eMonti M, Ferrari G, Gazzurelli L, Bugatti M, Facchetti F, Vermi W. Plasmacytoid dendritic cells at the forefront of anti-cancer immunity: rewiring strategies for tumor microenvironment remodeling. J Exp Clin Cancer Res. 2024;43(1):196.\u003c/li\u003e\n\u003cli\u003eMurthy HS, Zhang MJ, Chen K, Ahmed S, Deotare U, Ganguly S, et al. Allogeneic hematopoietic cell transplantation for blastic plasmacytoid dendritic cell neoplasm: a CIBMTR analysis. Blood Adv. 2023;7(22):7007-16.\u003c/li\u003e\n\u003cli\u003eMandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315-6.\u003c/li\u003e\n\u003cli\u003eBalachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the eye. Lancet Oncol. 2015;16(4):e173-80.\u003c/li\u003e\n\u003cli\u003eSteyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128-38.\u003c/li\u003e\n\u003cli\u003eLi T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017;77(21):e108-e10.\u003c/li\u003e\n\u003cli\u003eRu B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35(20):4200-2.\u003c/li\u003e\n\u003cli\u003eGyőrffy B. Transcriptome-level discovery of survival-associated biomarkers and therapy targets in non-small-cell lung cancer. Br J Pharmacol. 2024;181(3):362-74.\u003c/li\u003e\n\u003cli\u003eGyőrffy B. Integrated analysis of public datasets for the discovery and validation of survival-associated genes in solid tumors. Innovation (Camb). 2024;5(3):100625.\u003c/li\u003e\n\u003cli\u003eTiberio L, Del Prete A, Schioppa T, Sozio F, Bosisio D, Sozzani S. Chemokine and chemotactic signals in dendritic cell migration. Cell Mol Immunol. 2018;15(4):346-52.\u003c/li\u003e\n\u003cli\u003eLey TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A, et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368(22):2059-74.\u003c/li\u003e\n\u003cli\u003eAlizadeh AA, Aranda V, Bardelli A, Blanpain C, Bock C, Borowski C, et al. Toward understanding and exploiting tumor heterogeneity. Nat Med. 2015;21(8):846-53.\u003c/li\u003e\n\u003cli\u003eSharma SV, Lee DY, Li B, Quinlan MP, Takahashi F, Maheswaran S, et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell. 2010;141(1):69-80.\u003c/li\u003e\n\u003cli\u003eWu J, Zhou Z, Li J, Liu H, Zhang H, Zhang J, et al. CHD4 promotes acquired chemoresistance and tumor progression by activating the MEK/ERK axis. Drug Resist Updat. 2023;66:100913.\u003c/li\u003e\n\u003cli\u003eWang Y, Chen Y, Bao L, Zhang B, Wang JE, Kumar A, et al. CHD4 Promotes Breast Cancer Progression as a Coactivator of Hypoxia-Inducible Factors. Cancer Res. 2020;80(18):3880-91.\u003c/li\u003e\n\u003cli\u003eGallo M, Coutinho FJ, Vanner RJ, Gayden T, Mack SC, Murison A, et al. MLL5 Orchestrates a Cancer Self-Renewal State by Repressing the Histone Variant H3.3 and Globally Reorganizing Chromatin. Cancer Cell. 2015;28(6):715-29.\u003c/li\u003e\n\u003cli\u003eDamm F, Oberacker T, Thol F, Surdziel E, Wagner K, Chaturvedi A, et al. Prognostic importance of histone methyltransferase MLL5 expression in acute myeloid leukemia. J Clin Oncol. 2011;29(6):682-9.\u003c/li\u003e\n\u003cli\u003eLiu L, Lin B, Yin S, Ball LE, Delaney JR, Long DT, et al. Arginine methylation of BRD4 by PRMT2/4 governs transcription and DNA repair. Sci Adv. 2022;8(49):eadd8928.\u003c/li\u003e\n\u003cli\u003eWu SY, Lai HT, Sanjib Banerjee N, Ma Z, Santana JF, Wei S, et al. IDR-targeting compounds suppress HPV genome replication via disruption of phospho-BRD4 association with DNA damage response factors. Mol Cell. 2024;84(2):202-20.e15.\u003c/li\u003e\n\u003cli\u003eLadang A, Rapino F, Heukamp LC, Tharun L, Shostak K, Hermand D, et al. Elp3 drives Wnt-dependent tumor initiation and regeneration in the intestine. J Exp Med. 2015;212(12):2057-75.\u003c/li\u003e\n\u003cli\u003eRosu A, El Hachem N, Rapino F, Rouault-Pierre K, Jorssen J, Somja J, et al. Loss of tRNA-modifying enzyme Elp3 activates a p53-dependent antitumor checkpoint in hematopoiesis. J Exp Med. 2021;218(3).\u003c/li\u003e\n\u003cli\u003eArruabarrena-Aristorena A, Maag JLV, Kittane S, Cai Y, Karthaus WR, Ladewig E, et al. FOXA1 Mutations Reveal Distinct Chromatin Profiles and Influence Therapeutic Response in Breast Cancer. Cancer Cell. 2020;38(4):534-50.e9.\u003c/li\u003e\n\u003cli\u003eGao S, Chen S, Han D, Wang Z, Li M, Han W, et al. Chromatin binding of FOXA1 is promoted by LSD1-mediated demethylation in prostate cancer. Nat Genet. 2020;52(10):1011-7.\u003c/li\u003e\n\u003cli\u003eZhang H, Zheng Y, Wang Z, Dong L, Xue L, Tian X, et al. KLF12 interacts with TRIM27 to affect cisplatin resistance and cancer metastasis in esophageal squamous cell carcinoma by regulating L1CAM expression. Drug Resist Updat. 2024;76:101096.\u003c/li\u003e\n\u003cli\u003eYang Y, Zhu Y, Zhou S, Tang P, Xu R, Zhang Y, et al. TRIM27 cooperates with STK38L to inhibit ULK1-mediated autophagy and promote tumorigenesis. Embo j. 2022;41(14):e109777.\u003c/li\u003e\n\u003cli\u003eDhalluin C, Carlson JE, Zeng L, He C, Aggarwal AK, Zhou MM. Structure and ligand of a histone acetyltransferase bromodomain. Nature. 1999;399(6735):491-6.\u003c/li\u003e\n\u003cli\u003eWu SY, Chiang CM. The double bromodomain-containing chromatin adaptor Brd4 and transcriptional regulation. J Biol Chem. 2007;282(18):13141-5.\u003c/li\u003e\n\u003cli\u003eLi X, Fu Y, Yang B, Guo E, Wu Y, Huang J, et al. BRD4 Inhibition by AZD5153 Promotes Antitumor Immunity via Depolarizing M2 Macrophages. Front Immunol. 2020;11:89.\u003c/li\u003e\n\u003cli\u003eLi L, Gao L, Zhou H, Shi C, Zhang X, Zhang D, et al. High Expression Level of BRD4 Is Associated with a Poor Prognosis and Immune Infiltration in Esophageal Squamous Cell Carcinoma. Dig Dis Sci. 2023;68(7):2997-3008. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ovarian cancer, Chromatin remodeling, Prognostic model, Nomogram, BRD4, Plasmacytoid dendritic cells","lastPublishedDoi":"10.21203/rs.3.rs-7030302/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7030302/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eChromatin remodeling-related genes (CRRGs) are essential regulators of gene expression and tumor behavior. Their role in shaping the immune microenvironment and influencing prognosis in ovarian cancer (OV) remains largely unexplored. This study aimed to develop a CRRG-based prognostic signature and investigate its association with immune infiltration, particularly plasmacytoid dendritic cells (pDCs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e We integrated transcriptomic and clinical data from TCGA (n = 228) and ICGC (n = 111) ovarian cancer cohorts. Differentially expressed CRRGs associated with overall survival were identified and used to construct a prognostic signature via LASSO and Cox regression. Immune infiltration was analyzed using ssGSEA and validated by multiplex immunofluorescence (mIF). Correlations between BRD4 expression, pDC infiltration, and chemokine profiles were assessed using public databases and clinical specimens.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eAn 11-gene CRRG-based immune-associated signature was established, effectively stratifying patients into high- and low-risk groups with significantly different overall survival in both the TCGA and ICGC cohorts. The risk score was an independent prognostic factor. Immune analyses revealed that high-risk patients exhibited reduced pDC infiltration and lower activation of antigen presentation-related pathways. BRD4 was identified as a key gene negatively correlated with pDC levels across datasets. High BRD4 expression was associated with poor survival and decreased expression of multiple pDC-attracting chemokines. mIF staining confirmed the inverse correlation between BRD4 expression and BDCA2\u003csup\u003e+\u003c/sup\u003e pDC infiltration in OV tumor tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eThis study proposes a novel CRRG-based prognostic signature linked to immune features in OV and highlights BRD4 as a potential regulator of pDC infiltration through suppression of chemokine expression. These findings provide insights into the interplay between epigenetic regulation and the immune landscape in OV, although the implications for immunotherapy responsiveness warrant further investigation.\u003c/p\u003e","manuscriptTitle":"BRD4-Associated Chromatin Remodeling Signature Links Epigenetic Regulation to Immune Landscape in Ovarian Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 14:11:00","doi":"10.21203/rs.3.rs-7030302/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-25T14:32:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-25T14:18:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T15:05:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85715828003231319883080115433576320873","date":"2025-10-09T15:02:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339511335985744244295238183273624769962","date":"2025-10-09T12:48:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105419574782778962104108517326731482615","date":"2025-10-04T01:22:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303194426199743715252451638990948874511","date":"2025-09-14T09:43:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T13:44:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-09T11:43:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T05:50:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Ovarian Research","date":"2025-07-02T14:15:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c6c5c8ab-bc3b-428f-9d3d-79e6176428e0","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:04:32+00:00","versionOfRecord":{"articleIdentity":"rs-7030302","link":"https://doi.org/10.1186/s13048-026-02022-z","journal":{"identity":"journal-of-ovarian-research","isVorOnly":false,"title":"Journal of Ovarian Research"},"publishedOn":"2026-02-11 15:58:31","publishedOnDateReadable":"February 11th, 2026"},"versionCreatedAt":"2025-07-18 14:11:00","video":"","vorDoi":"10.1186/s13048-026-02022-z","vorDoiUrl":"https://doi.org/10.1186/s13048-026-02022-z","workflowStages":[]},"version":"v1","identity":"rs-7030302","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7030302","identity":"rs-7030302","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}